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Radiology has been essential to accurately diagnosing diseases and assessingresponses to treatment. The challenge however lies in the shortage ofradiologists globally. As a response to this, a number of ArtificialIntelligence solutions are being developed. The challenge ArtificialIntelligence radiological solutions however face is the lack of a benchmarkingand evaluation standard, and the difficulties of collecting diverse data totruly assess the ability of such systems to generalise and properly handle edgecases. We are proposing a radiograph-agnostic platform and framework that wouldallow any Artificial Intelligence radiological solution to be assessed on itsability to generalise across diverse geographical location, gender and agegroups.

A Standardized Radiograph-Agnostic Framework and Platform For Evaluating AI Radiological Systems